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Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography.

Tuncay V, Prakken N, van Ooijen PM, Budde RP, Leiner T, Oudkerk M - Biomed Res Int (2015)

Bottom Line: Time required for manual and semiautomatic segmentations was recorded.Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively.Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

View Article: PubMed Central - PubMed

Affiliation: Center for Medical Imaging North East Netherlands (CMI-NEN), Department of Radiology, University of Groningen, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, Netherlands.

ABSTRACT

Objective: The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets.

Methods: The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are manually selected to create an initial contour. The contour moves automatically towards the edge of aortic AVA to obtain a segmentation of the AVA. AVA was segmented semiautomatically and manually by two observers in multiphase cardiac CT scans of 25 patients. Validation of the algorithm was obtained by comparing to Transthoracic Echocardiography (TTE). Intra- and interobserver variability were calculated by relative differences. Differences between TTE and MDCT manual and semiautomatic measurements were assessed by Bland-Altman analysis. Time required for manual and semiautomatic segmentations was recorded.

Results: Mean differences from TTE were -0.19 (95% CI: -0.74 to 0.34) cm(2) for manual and -0.10 (95% CI: -0.45 to 0.25) cm(2) for semiautomatic measurements. Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively.

Conclusion: Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

No MeSH data available.


Bland-Altman plot between the TTE and semiautomatic AVA measurements.
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Related In: Results  -  Collection


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fig8: Bland-Altman plot between the TTE and semiautomatic AVA measurements.

Mentions: Comparisons of the manual and semiautomatic measurements with TTE results were performed using Bland-Altman plots; mean difference between TTE and MDCT results was −0.19 (95% CI: −0.74 to 0.34) cm2 for manual and −0.10 (95% CI: −0.45 to 0.25) cm2 for semiautomatic measurements (Figures 7 and 8). The differences were significantly different from 0 (p = 0.001 for manual and p = 0.007 for semiautomatic measurements) indicating a bias. Both mean difference and the confidence interval are smaller in the comparison of TTE and semiautomatic measurements which indicates that semiautomatic measurements are closer to the TTE measurements than to the manual measurements.


Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography.

Tuncay V, Prakken N, van Ooijen PM, Budde RP, Leiner T, Oudkerk M - Biomed Res Int (2015)

Bland-Altman plot between the TTE and semiautomatic AVA measurements.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4499628&req=5

fig8: Bland-Altman plot between the TTE and semiautomatic AVA measurements.
Mentions: Comparisons of the manual and semiautomatic measurements with TTE results were performed using Bland-Altman plots; mean difference between TTE and MDCT results was −0.19 (95% CI: −0.74 to 0.34) cm2 for manual and −0.10 (95% CI: −0.45 to 0.25) cm2 for semiautomatic measurements (Figures 7 and 8). The differences were significantly different from 0 (p = 0.001 for manual and p = 0.007 for semiautomatic measurements) indicating a bias. Both mean difference and the confidence interval are smaller in the comparison of TTE and semiautomatic measurements which indicates that semiautomatic measurements are closer to the TTE measurements than to the manual measurements.

Bottom Line: Time required for manual and semiautomatic segmentations was recorded.Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively.Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

View Article: PubMed Central - PubMed

Affiliation: Center for Medical Imaging North East Netherlands (CMI-NEN), Department of Radiology, University of Groningen, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, Netherlands.

ABSTRACT

Objective: The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets.

Methods: The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are manually selected to create an initial contour. The contour moves automatically towards the edge of aortic AVA to obtain a segmentation of the AVA. AVA was segmented semiautomatically and manually by two observers in multiphase cardiac CT scans of 25 patients. Validation of the algorithm was obtained by comparing to Transthoracic Echocardiography (TTE). Intra- and interobserver variability were calculated by relative differences. Differences between TTE and MDCT manual and semiautomatic measurements were assessed by Bland-Altman analysis. Time required for manual and semiautomatic segmentations was recorded.

Results: Mean differences from TTE were -0.19 (95% CI: -0.74 to 0.34) cm(2) for manual and -0.10 (95% CI: -0.45 to 0.25) cm(2) for semiautomatic measurements. Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively.

Conclusion: Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

No MeSH data available.